Undaria pinnatifida, popularly known as wakame, is a brown kelp that thrives in cold water. Given its nutrient-rich composition and bioactives, it has extensive application prospects in various industries, e.g., food, cosmetics, animal feed, and bioremediation ^1,2. One emerging use is biofuel production ³. In this connection, research on economically and technically viable approaches to successfully commercializing third-generation renewable biofuels is crucial ⁴. Seaweed is generally a more efficient converter of solar energy because its cells grow in an aqueous suspension, which gives them ready access to CO₂, water, and other nutrients ⁵. Its high oil content relative to its dry weight qualifies it as a candidate for conversion through various processes, e.g., transesterification, pyrolysis, and direct combustion ⁶. Based on laboratory data, the higher heating value (HHV) of U. pinnatifida was 2553.5 kcal kg⁻¹, which is suitable for biofuel production. Still, this value is lower than that of wood-derived biomass (≈4500–5000 kcal kg⁻¹). However, the volatile fraction (50.2%) of U. pinnatifida makes it feasible for gasification with high generation of combustible gases (H₂, CO), a process that is key to the synthesis of renewable synthetic gas. This overcomes the drawback of low HHV by expanding the scope of energy products. Thus, it could be an up-and-coming option for local circular bioenergy systems in coastal areas, as it can grow in waters unsuitable for agriculture and does not compete with food crops, ideal for biodiesel, bioethanol, biogas, and biohydrogen. Further studies are needed to develop and test existing hypotheses.

In the production of controlled fermented beverages, thermal processing is an essential step to ensure microbiological safety, while also facilitating starch gelatinization in maize-based beverages. However, it can degrade antioxidant compounds from purple maize, such as anthocyanins and polyphenols. The aim of this study was to identify an adequate thermal treatment for a purple maize beverage that maximizes antioxidant capacity while ensuring its microbiological safety prior to controlled fermentation.

A 1:9 ratio of purple maize to water was soaked for 12 hours. The mixture was then milled and strained. The resulting liquid was subjected to different heat treatments: a no-heat control (A_c), 85 °C for 5 minutes (A₁), 85 °C for 10 minutes (A₂), and 121 °C for 15 minutes (A₃). Total mesophilic bacteria, total coliforms, and yeasts were quantified by plate count. The pH, total soluble solids (TTSs), color based on the CIELAB parameters, and apparent viscosity were measured. Total phenolic compounds (TPCs), monomeric anthocyanins (MAAs), and in vitro antioxidant capacity were assessed via DPPH and ABTS assays. Statistical analysis was performed using one-way ANOVA, followed by Tukey's test (p<0.05).

All heat treatments ensured the microbiological safety of the beverage. A₁ exhibited the highest apparent viscosity. The total color difference (ΔE) for samples A₁ and A₂ indicated minimal perceptible color changes. TPCs, MAAs, and the resulting antioxidant capacity were all significantly higher in sample A₁. Therefore, the A₁ treatment effectively enhances the extraction of TPCs and MAAs, leading to the highest antioxidant capacity. However, its viscosity requires further evaluation for consumer acceptability.

Alzheimer's disease is one of the most frequent neurodegenerative diseases, leading to a disruption in the cognitive process of the human brain. Using this type of dataset to implement machine learning and deep learning techniques is a common approach for detecting and classifying Alzheimer's disease. In this study, we addressed primary research problems, including early diagnosis and accurate classification of Alzheimer's disease, effective preprocessing of imaging and non-imaging data, and identifying the most accurate modelling strategy between machine learning and deep learning techniques. We made an effort to present an advanced neuroimaging-based analysis of Alzheimer's disease early detection, implementing various machine learning and deep learning techniques. We collected our dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). For our work, we extracted attributes such as structural MRI, clinical assessments, and cognitive data from our collected dataset. In this study, we employed machine learning and deep learning techniques separately to evaluate their precision and accuracy in detecting and classifying Alzheimer's detection, which led to identifying the most optimized results. We utilized a custom CNN and Self-Attention (SA) model, along with DenseNet, ResNet-50, and VGG-16, to implement deep learning techniques. For machine learning techniques, we employed Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), CatBoost, and Gradient Boosting models. To compare our method with state-of-the-art strategies, we used metrics such as accuracy, precision, and F1 scores. Our approach outperformed existing machine learning and deep learning models. In our approach, CNN and Self-Attention model achieved an accuracy of 98.20%.

Ultrasonic nebulization generates a micro-mist that rapidly wets surfaces with minimal liquid load, making it attractive as a rinse-free postharvest step for delicate, cut produce. This study evaluated an ultrasonic nebulization treatment for sliced mushrooms using 5 ppm of chlorine dioxide (ClO₂), 80 ppm of peracetic acid (PAA), water, and a non-nebulized control. Slices were treated in a sealed chamber for a total of 2 min (two cycles of 1-min ON/2-min OFF) using nebulizers with an ultrafine spray aperture (~5 μm) and stored at 5 °C and 85% RH for 11 days. Weight loss, firmness, color, browning index, overall appearance, pH, total phenolics, antioxidant capacity, and microbiology counts (total aerobic mesophiles, yeasts, and molds) were monitored every 3-4 days. Weight loss was higher for PAA and water-treated slices (p<0.05). No significant differences were detected among treatments for firmness, browning index, color (chroma), or overall appearance across storage (p<0.05). PAA showed the highest pH at the end of storage. Total phenolics and antioxidant capacity were higher in ClO₂-treated slices throughout storage, except on day 0, when PAA showed the greatest values (p < 0.05). Microbiological analyses showed modest but significant decreases in total aerobic counts for both ClO₂ and PAA, as well as for yeasts and molds with ClO₂, compared to the control (p < 0.05). Overall, ultrasonic nebulization with ClO₂ or PAA showed potential to reduce microbial load while maintaining sliced-mushroom quality, with ClO₂ supporting higher total phenolics and antioxidant capacity. Future work should optimize dose and duty cycle to balance antimicrobial effect with preservation of antioxidants.

Phishing attacks remain a dominant and evolving cybersecurity threat, exploiting deceptive techniques to compromise user credentials and sensitive data. Traditional detection systems, often rule-based or reliant on manually engineered features, struggle to cope with the dynamic nature of phishing patterns. This study proposes a hybridized deep learning model that integrates Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BiGRU) to effectively detect phishing websites. CNN is employed for spatial feature extraction from URL and HTML structures, while BiGRU captures temporal dependencies, enabling a comprehensive understanding of phishing behaviors. The model processes a rich dataset of 80,000 website instances—sourced from PhishTank, OpenPhish, and other repositories, and applies Min-Max scaling during preprocessing to normalize feature values. A dual-pathway architecture fuses spatial and sequential features into a unified representation, enhancing detection performance. Experimental evaluations using train–test split and 5-fold stratified cross-validation demonstrate outstanding results, achieving 99.97% accuracy, 99.98% recall, and 99.96% specificity. The model further exhibits strong generalizability when tested on an external dataset, reinforcing its robustness across diverse phishing patterns. Comparative analysis with existing deep learning methods, including CNN-LSTM and CNN-BiLSTM, confirms that the CNN-BiGRU architecture delivers superior performance with reduced false positives and false negatives. This work highlights the potential of hybrid deep learning frameworks in building resilient, scalable, and real-time phishing detection systems suited for deployment in modern web security infrastructures.

Stingless bee honey (SBH) contains bioactive compounds which are influenced by its botanical origin, and these constituents are closely associated with its health-promoting properties. Interest in monofloral honey has been increasing owing to its distinctive sensory characteristics and relatively consistent nutritional composition and higher market value. The growing burden of cardiometabolic disease underscores the need for more studies that examine the inhibition of enzymes relevant to these pathways. This study analysed 17 SBH from 13 botanical origins (Acacia, coconut, cocoa, durian, dwarf mountain pine, elderberry, golden shower tree, Mexican creeper, rose myrtle, rubber tree, Singapore rhododendron, starfruit, sunflecks). Thirteen phenolic acids and 21 amino acids were quantified by HPLC. In vitro inhibition was evaluated against enzymes associated with post-prandial glycaemia (α-amylase, α-glucosidase), lipid digestion (pancreatic lipase, cholesterol esterase), cholesterol biosynthesis (HMG-CoA reductase), and blood-pressure regulation (ACE). Elderberry SBH showed the highest ACE inhibition (74.32 ± 0.69%), while Singapore rhododendron SBH exhibited the strongest HMG-CoA reductase inhibition (65.06 ± 1.95%). Sunflecks SBH gave the lowest IC₅₀ for cholesterol esterase (0.57 ± 0.01 µg/mL). PLS regression revealed that gallic acid was the primary contributor to ACE and HMG-CoA reductase inhibition, whereas both gallic acid and rutin were associated with stronger inhibition of digestive lipid-related enzymes. These data show that phenolic and amino-acid profiles differ by botanical origin, and this may help to explain the in vitro enzyme-inhibition patterns of SBH. The findings suggest the potential of SBH as a cardioprotective functional food with significance health benefits.

Abstract

Vanillin is a well-known natural flavoring agent with remarkable biological and pharmacological properties, including antimicrobial and antioxidant activities. The aim of this study was to synthesize nitrovanillin from vanillin and evaluate its antioxidant, antibacterial, and antifungal activities.

Nitrovanillin was synthesized using nitric acid as a nitrating agent, with dichloromethane (DCM) as the solvent, resulting in a favorable yield. The compound was characterized by ultraviolet-visible (UV–Vis) spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, thin-layer chromatography (TLC), and melting point determination.

Antioxidant activity was assessed through several assays, including 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), ferric reducing antioxidant power (FRAP), phenanthroline, and silver nanoparticles (AgNPs). Antibacterial activity was evaluated by the agar disc diffusion method against Escherichia coli, Bacillus cereus, and Staphylococcus aureus, while antifungal activity was tested using the well diffusion method against Fusarium oxysporum grown on potato dextrose agar (PDA).

The results revealed that nitrovanillin exhibited strong antioxidant activity, particularly in the phenanthroline and AgNP assays. It showed potent antifungal activity with 90% inhibition of F. oxysporum at 200 μg/mL. Regarding antibacterial activity, vanillin inhibited S. aureus (14 mm) and E. coli (8 mm), while nitrovanillin demonstrated stronger inhibition against S. aureus (32 mm) and B. cereus (38 mm).

Overall, the introduction of a nitro group significantly enhanced the biological potential of vanillin, making nitrovanillin a promising compound for further investigation in biomedical and material applications.

Introduction

Over the past few decades, a significant quantity of scientific papers have been published. However, many of them remain unread, despite their potential level of novelty. Traditional dissemination formats, based on static papers or technical reports, are often inaccessible to policymakers, executives, or the general public. This paper proposes a vision for an intelligent framework that utilizes Generative AI agents to transform scientific papers into structured, audience-aware narratives, leveraging storytelling principles to enhance comprehension and engagement.

Methods

The proposed methodology introduces a multi-agent pipeline with three components. A Narrative Planner Agent first analyzes the paper and constructs a storyline using a three-act structure (Context, Problem, and Solution) [1]. A Generative Presenter Agent then produces a presentation tailored to the target audience. Finally, a Rubric-Based Evaluator Agent assesses the generated presentation on criteria such as clarity, scientific rigor, and audience alignment. The entire process is envisioned within an open-source workflow automation environment (e.g., n8n [2]) to ensure scalability and modularity.

Results

At this stage, the methodology is in the design phase. However, preliminary texts in other domains have demonstrated that Generative AI can produce audience-based stories originating from domain-specific material [3].

Conclusions

This work outlines a path toward AI-driven scientific storytelling, where research is transformed into meaningful, contextualized narratives. Such systems could pave the way towards interdisciplinary communication, science education, and public engagement in applied sciences.

Artemisia arborescens (A. arborescens) holds a long-standing place in Moroccan traditional medicine and is increasing attention for its pharmacological and therapeutic potential. This study evaluates the phytochemical composition of aqueous and hydroethanolic extracts, along with their antioxidant and anti-inflammatory properties.

Spectrophotometric analysis was used to quantify bioactive compounds. Antioxidant activity was assessed through total antioxidant capacity (TAC), while radical scavenging ability was measured using 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) and 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH), and anti-inflammatory effects were evaluated by inhibition of bovine serum albumin (BSA) denaturation.

The aqueous extract contained phenolics (1.575 ± 0.97 mg AGE/g_DM, flavonoids (18.51 ± 0.64 mg QE/g_DM), tannins (0.615 ± 0.150 mg CE/g_DM), and flavonols (2.99 ± 0.09 mg QE/g DM). Antioxidant activity, as determined by TAC showed an IC₅₀ of 2.68 ± 0.29 mg/mL, while both DPPH and ABTS values exceeded 10 mg/mL. BSA inhibition reached 0.964 ± 0.133 mg/mL.

In comparison, the hydroethanolic extract exhibited higher levels of phenolics (21.30 ± 1.10 mg AGE/g_DM), flavonoids (24.80 ± 0.80 mg QE/g_DM), tannins (0.550 ± 0.120 mg CE/g_DM), and flavonols (4.10 ± 0.15 mg QE/g_DM). Antioxidant activity was stronger, with TAC IC₅₀ of 1.262 ± 0.89 mg/mL, ABTS scavenging activity was 8.127 ± 0.903 mg/mL, while DPPH remained above 10 mg/mL. BSA inhibition was 0.560 ± 0.109 mg/mL.

Overall, the hydroethanolic extract displayed a richer phytochemical profile and greater biological activity than the aqueous extract. These findings support the pharmacological potential of A. arborescens and underscore the need for further mechanistic and in vivo studies.

The recent price increases in cocoa occurring worldwide have led to the search for alternative cocoa-based products; carob is being used as a nutritious sweetener alternative to cocoa and cocoa-based products (e.g., chocolate). The present study evaluated the physicochemical, microbiological, nutritional, sensorial, phenolic, and antioxidant profiles of a carob-based spreadable product in comparison with two commercially available similar chocolate–hazelnut spreads. Moreover, the economic impact of the newly developed product was also estimated. The carob-based product was slightly more acidic (pH 5.3) and had an increased water activity (a_w = 0.67) when compared to the commercial chocolate spreads. Ongoing microbiological analysis and sensory evaluation suggest that the developed product has a shelf life of over six months. In terms of nutritional value, the product is suitable for vegans, as it contains no added dairy, eggs, or preservatives, while it is also high in fiber and very low in sodium. Total phenolic (TPC) and total flavonoid (TFC) contents were quantified spectrophotometrically, while antioxidant capacity was assessed using the DPPH radical scavenging assay. Comparatively, the developed carob-based product exhibited higher TPC but lower TFC, whereas antioxidant activity was the same as the other two cocoa-based spreads. Market analysis indicates that there is a considerable margin of profit for the developed product against competition. In conclusion, this research denotes the functional properties of carob-based products with enhanced potential health benefits compared to conventional cocoa-based products. Finally, it also highlights the better nutritional profile and the competitiveness that the carob-based products exhibit when compared to their cocoa-based counterparts.